My Journey in Computational Cardiology and Atherosclerosis: An Aerospace Engineer Working at Hospital
Cardiovascular disease remains the leading cause of morbidity and mortality due to aging populations and risk factors associated with life habits. As the most widely adopted therapy in cardiology, percutaneous intervention has made great strides owing to its cost effectiveness and versatility. Innovation in this space has long benefited from mechanism-based computational modeling, and more recently from the introduction of deep learning techniques. It is crucial to appreciate the benefits associated with computational modeling and bioinformatics approaches now more than before as the newest generation of bioresorbable vascular scaffolds exhibit poor clinical performance. Among several other issues, the community believes that the population level approach of performance assessment has been exhausted. The commercial failure of a pioneer device, Absorb, which was attributed to unexpected higher risk in a subpopulation of clinical cases, could have been provisioned had they employed more extensively subject-specific models and physics-governed computational modeling.
The evolution of personalized digital medicine towards enhanced prevention techniques, diagnosis, and therapy is promisingly feasible nowadays due to substantial advances in imaging modalities, mathematical models, and computational power. Moreover, there is an increasing interest to use subject-specific computational models to complement experimental research in medicine. The ultimate goal for these engineering toolkits is to be routinely used in real-time clinical platforms and hopefully adopted by regulatory officials as in silico clinical trials. Toward this end, there is a clear need to integrate ideas, form close collaborations between scientists, engineers, and clinicians, and develop rigorous tools for modeling, analysis, and synthesis of complex biological systems. My research agenda contributes to fulfill this vision by addressing emerging problems from new angles, incorporating powerful computational tools, and capitalizing on the synergy between mechanical engineering, translational medicine, and informatics. this approach has provided new perspective into the contextual biocompatibility of medical implants relative to mechanical environment to which they are exposed. To highly optimize contemporary interventional practices, advances in mechanical design, drug use, and deployment strategies are required along with biomaterial innovations. This novel approach has created both challenges and opportunities to further improve clinical performance. The aim of this talk is to introduce this vision, discuss the achievements and challenges, and introduce my future research agenda. The focus would be to briefly discuss how advanced medical devices are expected to perform (mechanically, in particular) rather than merely exist in complex environment of living substrates to dictate clinical efficacy and safety.
Date and Time
Location
Hosts
Registration
- Date: 10 Mar 2022
- Time: 12:00 PM to 01:00 PM
- All times are (UTC-05:00) Eastern Time (US & Canada)
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- Boston, Massachusetts
- United States
- Starts 01 March 2022 12:00 AM
- Ends 10 March 2022 12:45 PM
- All times are (UTC-05:00) Eastern Time (US & Canada)
- No Admission Charge
Speakers
Dr. Farhad R. Nezami of Brigham and Women's Hospital, Harvard Medical School
Biography:
Dr. Farhad Rikhtegar Nezami, is currently Lead Investigator and Faculty Member at the Division of Thoracic and Cardiac Surgery of Brigham and Women’s Hospital under Harvard Medical School and affiliated with Harvard-MIT Biomedical Engineering Center, located at the Institute for Medical Engineering and Science of MIT. He has got his PhD in Mechanical engineering from ETH Zurich, where he conducted research on hemodynamics and drug transport in stented coronary arteries. He completed his master's and bachelor's degrees at Sharif University of Technology and Amirkabir University of Technology in Tehran, respectively. Dr. Rikhtegar Nezami’s research interests revolve around human pathophysiology, successful translation of preclinical experiments to clinical practices, design and optimization of medical devices, and developing engineering platforms to drive progress from the laboratory bench and computational toolkit to the patient's bedside. His work embraces precision medicine and personalized therapy and developing predictive/prognostic tools incorporating clinical data, computational tools, and machine-learning algorithms.